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Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

Author

Listed:
  • Yingxiang Liu

    (Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA)

  • Wei Ling

    (Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA)

  • Robert Young

    (Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA)

  • Jalal Zia

    (Cyrq Energy Inc., Salt Lake City, UT 84101, USA)

  • Trenton T. Cladouhos

    (Cyrq Energy Inc., Salt Lake City, UT 84101, USA)

  • Behnam Jafarpour

    (Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA)

Abstract

This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of exogenous variables, such as control adjustment and ambient temperature. In the LSDNN model, an encoder–decoder architecture was designed to capture cross-correlation among different measured variables. In addition, a latent space dynamic structure was proposed to propagate the dynamics in the latent space to enable prediction. The prediction power of the LSDNN was utilized for monitoring a geothermal power plant and detecting abnormal events. The model was integrated with principal component analysis (PCA)-based process monitoring techniques to develop a fault-detection procedure. The performance of the proposed LSDNN model and fault detection approach was demonstrated using field data collected from a geothermal power plant.

Suggested Citation

  • Yingxiang Liu & Wei Ling & Robert Young & Jalal Zia & Trenton T. Cladouhos & Behnam Jafarpour, 2022. "Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations," Energies, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2555-:d:784596
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    References listed on IDEAS

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